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Multiple Choice Question Answering (MCQA)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 31
보존된 평가 기록: 179

A multiple-choice question (MCQ) is composed of two parts: a stem that identifies the question or problem, and a set of alternatives or possible answers that contain a key that is the best answer to the question, and a number of distractors that are plausible but incorrect answers to the question.

In a k-way MCQA task, a model is provided with a question q, a set of candidate options O = {O1, . . . , Ok}, and a supporting context for each option C = {C1, . . . , Ck}. The model needs to predict the correct answer option that is best supported by the given contexts.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

Multiple Choice Question Answering (MCQA)

코드

https://github.com/allenai/dolma

https://github.com/bramiozo/PubScience

https://github.com/chrisociepa/allamo

https://github.com/coastalcph/eu-politics-llms

https://github.com/conceptofmind/PaLM-flax

https://github.com/dmis-lab/olaph

https://github.com/eternityyw/tram-benchmark

https://github.com/fatyin88/MCQA_project

https://github.com/flagalpha/llama2-chinese

https://github.com/foundation-model-stack/fms-fsdp

https://github.com/glb400/Toy-RecLM

https://github.com/google/paxml

https://github.com/idiap/abroad-re

https://github.com/karanchahal/play-with-vcr

https://github.com/karpathy/llama2.c

https://github.com/llamafamily/llama-chinese

https://github.com/lucidrains/CoCa-pytorch

https://github.com/lucidrains/PaLM-jax

https://github.com/lucidrains/PaLM-pytorch

https://github.com/m42-health/med42

https://github.com/meetyou-ai-lab/can-mc-evaluate-llms

https://github.com/ninglab/ecellm

https://github.com/nkluge-correa/teenytinyllama

https://github.com/nyu-mll/squality

https://github.com/open-finance-lab/finlora

https://github.com/rijgersberg/geitje

https://github.com/rowanz/r2c

https://github.com/rvlopes/gloria

https://github.com/siqims/ARC-Challenge

https://github.com/squeezeailab/squeezellm

https://github.com/usyd-fsalab/fp6_llm

https://github.com/xuetianci/pacit

https://github.com/xverse-ai/xverse-13b

https://github.com/xzhang97666/alpacare

https://github.com/yangletliu/finlora

https://github.com/young-geng/easylm